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dchiji-ntt avatar

dchiji-ntt/iterand

0
View on GitHub↗
10 stars·2 forks·Python·4 vuesarxiv.org/abs/2106.09269↗

Iterand

by Daiki Chijiwa*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue

Features

  • Weight Pruning - Iterative randomization for pruning randomly initialized networks.

Historique des stars

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Questions fréquentes

Que fait dchiji-ntt/iterand ?

by Daiki Chijiwa\*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue

Quelles sont les fonctionnalités principales de dchiji-ntt/iterand ?

Les fonctionnalités principales de dchiji-ntt/iterand sont : Weight Pruning.

Quelles sont les alternatives open-source à dchiji-ntt/iterand ?

Les alternatives open-source à dchiji-ntt/iterand incluent : tencent/pocketflow — PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format… chrundle/biprop — This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized… dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspective — [ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective. densoitlab/bitprune — This is the official repo for ICLR 2023 Paper "Bit-Pruning: A Sparse Multiplication-Less Dot-Product" Yusuke Sekikawa… dingxiaoh/gsm-sgd — This repository contains the codes for the following NeurIPS-2019 paper. boone891214/sanity-check-lth — Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the…

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  • tencent/pocketflowAvatar de Tencent

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    PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format optimization. It provides a system for reducing the size and complexity of neural networks to improve inference efficiency, featuring a dedicated engine for knowledge distillation and a mobile model optimizer. The framework differentiates itself through an automated hyperparameter tuning system that uses reinforcement learning and statistical models to determine optimal compression ratios and layer-wise bit allocation. It also includes a distributed training system that utilizes mu

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  • chrundle/bipropAvatar de chrundle

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    This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized network that achieves performance comparable to, and sometimes better than, a weight-optimized network. The resulting binarized and pruned networks that achieve comparable performance…

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  • dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspectiveAvatar de dem123456789

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  • boone891214/sanity-check-lthAvatar de boone891214

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    Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

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  • Voir les 30 alternatives à Iterand→